viznoir

viznoir

VTK is all you need. Cinema-quality science visualization for AI agents.

[CI] [PyPI] [Python] [License: MIT] [Mentioned in Awesome VTK]

What it does

An MCP server that gives AI agents full access to VTK's rendering pipeline — no ParaView GUI, no Jupyter notebooks, no display server. Your agent reads simulation data, applies filters, renders cinema-quality images, and exports animations, all headless.

Works with: Claude Code · Cursor · Windsurf · Gemini CLI · any MCP client

Quick Start

1. Install

pip install viznoir
# With optional extraspip install "viznoir[mesh]"       # meshio + trimesh (50+ formats)pip install "viznoir[composite]"  # Pillow + matplotlib (split_animate)pip install "viznoir[all]"        # everything

Requires Python ≥3.10. VTK wheel auto-installed (EGL headless rendering supported).

2. Verify

mcp-server-viznoir --help    # server entry pointpython -c "import viznoir; print(viznoir.__version__)"

3. Use with an MCP client

Add to your MCP client config (claude_desktop_config.json, ~/.cursor/mcp.json, etc.):

{  "mcpServers": {    "viznoir": {      "command": "mcp-server-viznoir",      "env": {        "VIZNOIR_DATA_DIR": "/path/to/your/simulation/data",        "VIZNOIR_OUTPUT_DIR": "/path/to/output"      }    }  }}

Then ask your AI agent:

"Open cavity.foam, render the pressure field with cinematic lighting, then create a physics decomposition story."

4. Or use as a Python library (advanced)

All tool implementations are importable as async functions. You provide a VTKRunner and await the result:

import asynciofrom viznoir.core.runner import VTKRunnerfrom viznoir.tools.inspect import inspect_data_implfrom viznoir.tools.render import render_impl
async def main():    runner = VTKRunner()
    meta = await inspect_data_impl(file_path="cavity.foam", runner=runner)    print(meta["fields"], meta["timesteps"])
    result = await render_impl(        file_path="cavity.foam",        field_name="p",        runner=runner,        colormap="Cool to Warm",        camera="isometric",        width=1920, height=1080,        output_filename="pressure.png",    )    print(result.file_path)
asyncio.run(main())

See docs for the full tool reference.

Capabilities

22 tools · 12 resources · 4 prompts · 50+ file formats (OpenFOAM, VTK, CGNS, Exodus, STL, glTF, …)

Showcase — 10 Domains, One Pipeline

Every frame below is a single MCP tool call. No GUI, no post-processing, no ParaView. Annotations are rendered inside the 3D scene via VTK-native text actors and leader lines — no Photoshop, no matplotlib overlay.

Physics-Aware Animations

Seven presets convert raw simulation data into publication-ready motion — each binds a rendering primitive to a physical phenomenon.

Story Composition (compose_assets)

Layouts: story (vertical narrative) · grid (N×M comparison) · slides (16:9 keynote) · video (MP4 with transitions)

Full interactive gallery: https://kimimgo.github.io/viznoir/#showcase

Architecture

  prompt                    "Render pressure from cavity.foam"    │  MCP Server                22 tools · 12 resources · 4 prompts    │  VTK Engine                readers → filters → renderer → camera    │                       EGL/OSMesa headless · cinematic lighting  Physics Layer             topology analysis · context parsing    │                       vortex detection · stagnation points  Animation                 7 physics presets · easing · timeline    │                       transitions · compositor · video export  Output                    PNG · WebP · MP4 · GLTF · LaTeX

Numbers

Documentation

Homepage: kimimgo.github.io/viznoir

Developer docs: kimimgo.github.io/viznoir/docs — full tool reference, domain gallery, architecture guide

License

MIT